Taxpayers, such as, for example, individuals and companies, may file income tax returns yearly. Based on a taxpayer's gross income and possible deductions and exemptions available, a taxpayer may either owe taxes or receive a refund for taxes paid during the year. Accordingly, it is common for a taxpayer to attempt to reduce his taxable income, and associated income tax liability, to either pay as little tax as possible or receive as large a tax refund as possible.
In order to reduce the amount of income tax liability, a taxpayer may, for example, deduct expenses that are not legally available, claim more dependents than allowed by law, fail to report all earned income, etc. These are examples of fraudulent actions that a taxpayer may take to reduce income tax liability. In addition, a taxpayer may purchase or steal one or more false identities and file fraudulent tax returns in an effort to receive a tax refund. These are other examples of fraudulent activity.
Such examples are prevalent and are increasingly common and difficult to catch by the tax receiving agency. For example, over 130 million tax returns were filed with the Internal Revenue Service (“IRS”) in 2007. A corresponding number of tax returns were also filed in the corresponding state(s) of residence for each taxpayer. Therefore, the large volume of filed tax returns results in a large number of fraudulent tax returns that either improperly reduce the correct amount of taxes owed or improperly increase the amount of refunds owed.
One way to identify a fraudulent tax return would be to audit the return, e.g., by comparing it to previously filed returns, historical data, and other relevant information. When applying analytics to determine a fraud risk of a particular transaction (e.g. tax return processing), it is desirable to apply one or more risk rules to data contained in the transaction and/or a broad range of stored historical data. Current systems typically conduct all calculations required for risk scoring at the time of a transaction, e.g., when a tax return is received or when a batch of returns are audited.
In current systems, there is a need to employ increasingly sophisticated analytics as part of transaction processing to detect and prevent possible fraud. However, the ability to employ analytic techniques is often limited by the need to maintain very high standards of performance and reliability in the transaction processing system because sophisticated analytics require substantial processing power and add substantial complexity if executed in real time as a part of the transaction processing.